Original Research ARTICLE
EEG-based mental workload neurometric to evaluate the impact of different traffic and road conditions in real driving settings
- 1Dipartimento di Medicina Molecolare, Sapienza Università di Roma, Italy
- 2BrainSigns, Italy
- 3Laboratorio di Immagini Neuroelettriche e Interfacce Cervello-Computer, Fondazione Santa Lucia (IRCCS), Italy
- 4Department of Anatomical, Histological, Medico-legal and Locomotor Sciences, Sapienza University of Rome, Italy
- 5Deep Blue (Italy), Italy
- 6Dipartimento di Ingegneria Civile, Chimica, Ambientale e dei Materiali, Università degli Studi di Bologna, Italy
- 7Department of Computer Science, Hangzhou Dianzi University, China
Car driving is considered a very complex activity, consisting of different concomitant tasks and subtasks, thus it is crucial to understand the impact of different factors, such as road complexity, traffic, dashboard devices, and external events on the driver’s behavior and performance. For this reason, in particular situations the cognitive demand experienced by the driver could be very high, inducing an excessive experienced mental workload and consequently an increasing of error commission probability. In this regard, it has been demonstrated that human error is the main cause of the 57 % of road accidents and a contributing factor in most of them. In this study, 20 young subjects were involved in a real driving experiment, performed under different traffic conditions (rush hour and not) and along different road types (main and secondary streets). Moreover, during the driving tasks different specific events, in particular a pedestrian crossing the road and a car entering the traffic flow just ahead of the experimental subject, were acted. A Workload Index based on the Electroencephalographic (EEG), i.e. brain activity, of the drivers has been employed to investigate the impact of the different factors on the driver’s workload. Eye-Tracking technology and subjective measures were also employed in order to have a comprehensive overview of the driver’s perceived workload and to investigate the different insights obtainable from the employed methodologies.
The employment of such EEG-based Workload index confirmed the significant impact of both traffic and road types on the drivers’ behavior (increasing their workload), with the advantage of being under real settings. Also, it allowed to highlight the increased workload related to external events while driving, in particular with a significant effect during those situations when the traffic was low. Finally, the comparison between methodologies revealed the higher sensitivity of neurophysiological measures with respect to Eye-Tracking and subjective ones.
In conclusion, such an EEG-based Workload index would allow to assess objectively the mental workload experienced by the driver, standing out as a powerful tool for research aimed to investigate drivers’ behavior and providing additional and complementary insights with respect to traditional methodologies employed within road safety research.
Keywords: Electroencephalography, Mental Workload, human factor, machine-learning, asSWLDA, neuroergonomics, Car driving, Road safety
Received: 16 Jul 2018;
Accepted: 05 Dec 2018.
Edited by:Muthuraman Muthuraman, Division of Movement Disorders and Neurostimulation, Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Germany
Reviewed by:Edmund Wascher, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Germany
Bahamn Nasseroleslami, Trinity College Dublin, Ireland
Copyright: © 2018 Di Flumeri, Borghini, Aricò, Sciaraffa, Lanzi, Pozzi, Vignali, Lantieri, Bichicchi, Simone and Babiloni. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: MD, PhD. Gianluca Di Flumeri, Dipartimento di Medicina Molecolare, Sapienza Università di Roma, Roma, 00185, Lazio, Italy, email@example.com